1. Field of the Invention
This invention relates generally to the field of data processing systems. More particularly, the invention relates to a system and method for sourcing a demand forecast within a supply chain management (“SCM”) system.
2. Description of the Related Art
Certain software applications are designed to comprehend complicated scheduling tasks. For example, a supply-chain-management (“SCM”) software application is typically designed to comprehend the resources in a supply chain (e.g., raw materials, manufacturing equipment, distribution, warehousing, etc) and schedule their usages (also referred to as “activities”) so that a specific “supply” of product can be provided at one or more places at specific times to meet the anticipated “demand” for the product.
More advanced SCM applications provide functions for intra- and inter-company supply chain planning and for scheduling and monitoring of associated supply chain processes. For example, the assignee of the present application has developed an advanced supply chain management platform known as the Advanced Planner & Optimizer (“APO”) which, as described in Gerhard Knolmayer, et al., SUPPLY CHAIN MANAGEMENT BASED ON SAP SYSTEMS (hereinafter “Knolmayer”), includes different modules for implementing various interrelated SCM functions. These modules include a demand planning (“DP”) module, a supply network planning (“SNP”) module, a production planning and detailed scheduling (“PP/DS”) module, a transportation planning/vehicle scheduling (“TPNS”) module and an available to promise (“ATP”) module. The following is a brief overview of each of these components.
The ability to accurately forecast demand is an important precondition to any production planning schedule. With this goal in mind, the DP module attempts to determine the demand for a product over a specified time period. By way of example, FIG. 1 illustrates a DP module 101 which has anticipated the demand for a particular product (P1) by a particular customer (C1) for January (100 units), February (150 units) and March (100 units). As indicated in FIG. 1, current demand planning techniques are largely based on empirical data 100 for a given product (e.g., historical demand data stored within an archiving system or data warehouse).
SNP and PP/DS both fall into the general category of “advanced planning and scheduling” or “APS” which involves the planning and scheduling of materials and resources within the supply chain. SNP differs from PP/DS in terms of the time horizon used for planning and scheduling. SNP is used for tactical (i.e., midterm) planning, whereas PP/DS is used for operational (i.e., short-term) planning. For example, a typical planning horizon for SNP may be in the range of 3-6 months whereas a typical planning horizon for PP/DS may be in the range of 1-7 days.
The TP/VS module employs techniques to optimize the delivery of products using different transportation routes and vehicles. It enables manufacturers, retailers, and logistics providers to coordinate transportation resources via the Internet and to synchronize transportation decisions and activities. The transportation planning component of TPNS focuses on medium- to long-term planning whereas the vehicle scheduling component focuses on short-term planning and routing.
Finally, the ATP module is responsible for determining whether a product can be promised by a specified delivery date in response to a customer request. If a given product is not in stock, ATP coordinates with other modules such as PP/DS to determine whether the product can be procured from alternate sources and/or manufactured in time to fulfil the customer request.
One problem which exists with current SCM systems is the lack of coordination between the demand planning component and the other system components. As mentioned above, the demand planning forecast is typically propagated through the supply chain based on empirical rules rather than in an optimized manner. Current demand planning forecasts do not factor variables such as material and resource constraints existing along various levels of the supply chain. As a result, current systems are incapable of intelligently sourcing the demand forecast in light of these constraints. In addition, once a demand forecast is sourced, current systems do not provide adequate coordination when sourcing subsequent sales orders entering the system. Accordingly, what is needed is an SCM system which employs more intelligent sourcing decisions using improved communication and coordination between demand planning and other SCM system components.